Streaming breakpoint for data tuples based on resource usage

ABSTRACT

A streams manager monitors data tuples processed by a streaming application represented by an operator graph. The streams manager includes a tuple breakpoint mechanism that allows defining a tuple breakpoint that fires based on resource usage by the data tuple. When the tuple breakpoint fires, one or more operators in the operator graph are halted according to specified halt criteria. Information corresponding to the breakpoint that fired is then displayed. The tuple breakpoint mechanism thus provides a way to debug a streaming application based on resource usage by data tuples.

BACKGROUND

1. Technical Field

This disclosure generally relates to streaming applications, and morespecifically relates to enhancing debugging of a streaming applicationusing breakpoints for data tuples based on resource usage.

2. Background Art

Streaming applications are known in the art, and typically includemultiple operators coupled together in an operator graph that processstreaming data in near real-time. An operator typically takes instreaming data in the form of data tuples, operates on the data tuplesin some fashion, and outputs the processed data tuples to the nextoperator. Streaming applications are becoming more common due to thehigh performance that can be achieved from near real-time processing ofstreaming data.

Many streaming applications require significant computer resources, suchas processors and memory, to provide the desired near real-timeprocessing of data. However, the workload of a streaming application canvary greatly over time. Allocating on a permanent basis computerresources to a streaming application that would assure the streamingapplication would always function as desired (i.e., during peak demand)would mean many of those resources would sit idle when the streamingapplication is processing a workload significantly less than itsmaximum. Furthermore, what constitutes peak demand at one point in timecan be exceeded as the usage of the streaming application increases. Fora dedicated system that runs a streaming application, an increase indemand may require a corresponding increase in hardware resources tomeet that demand.

Cloud-based streaming is known in the art. Known systems for cloud-basedstreaming do not monitor the resource usage of data tuples.

BRIEF SUMMARY

A streams manager monitors data tuples processed by a streamingapplication represented by an operator graph. The streams managerincludes a tuple breakpoint mechanism that allows defining a tuplebreakpoint that fires based on resource usage by the data tuple. Whenthe tuple breakpoint fires, one or more operators in the operator graphare halted according to specified halt criteria. Informationcorresponding to the breakpoint that fired is then displayed. The tuplebreakpoint mechanism thus provides a way to debug a streamingapplication based on resource usage by data tuples.

The foregoing and other features and advantages will be apparent fromthe following more particular description, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The disclosure will be described in conjunction with the appendeddrawings, where like designations denote like elements, and:

FIG. 1 is a block diagram of a cloud computing node;

FIG. 2 is a block diagram of a cloud computing environment;

FIG. 3 is a block diagram of abstraction model layers;

FIG. 4 is a block diagram showing some features of a streams manager;

FIG. 5 is a table showing some suitable examples of breakpoint criteriafor a tuple breakpoint based on resource usage by data tuples;

FIG. 6 is a table showing some suitable examples of tuple set criteria;

FIG. 7 is a table showing some suitable examples of halt criteria thatcould be specified for a tuple breakpoint;

FIG. 8 is a table showing some suitable examples of information thatcould be displayed when a tuple breakpoint fires;

FIG. 9 is a flow diagram of a method for defining a tuple breakpoint;

FIG. 10 is a flow diagram of a method for processing a tuple breakpointwhen it fires;

FIG. 11 is a block diagram showing a specific example of an operatorgraph corresponding to a streaming application;

FIG. 12 is a table showing tuple breakpoint criteria for the specificexample in FIG. 11; and

FIG. 13 is a table showing halt criteria for the tuple breakpoint shownin FIG. 12.

DETAILED DESCRIPTION

The disclosure and claims herein relate to a streams manager thatmonitors data tuples processed by a streaming application represented byan operator graph. The streams manager includes a tuple breakpointmechanism that allows defining a tuple breakpoint that fires based onresource usage by the data tuple. When the tuple breakpoint fires, oneor more operators in the operator graph are halted according tospecified halt criteria. Information corresponding to the breakpointthat fired is then displayed. The tuple breakpoint mechanism thusprovides a way to debug a streaming application based on resource usageby data tuples.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processor 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 130 can include computer system readable media in the formof volatile, such as random access memory (RAM) 134, and/or cache memory136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, dataarchival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes 352; RISC(Reduced Instruction Set Computer) architecture based servers 354;servers 356; blade servers 358; storage devices 360; and networks andnetworking components 362. In some embodiments, software componentsinclude network application server software 364 and database software366.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers368; virtual storage 370; virtual networks 372, including virtualprivate networks; virtual applications and operating systems 374; andvirtual clients 376.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning 378 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 380provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 382 provides access to the cloud computing environment forconsumers and system administrators. Service level management 384provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 386 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA. The management layer further includes astreams manager (SM) 350 as described herein. While the streams manager350 is shown in FIG. 3 to reside in the management layer 330, thestreams manager 350 actually may span other levels shown in FIG. 3 asneeded.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 388; software development and lifecycle management 390;virtual classroom education delivery 392; data analytics processing 394;transaction processing 396 and mobile desktop 398.

As will be appreciated by one skilled in the art, aspects of thisdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects of the presentinvention may take the form of a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a non-transitory computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

FIG. 4 shows one suitable example of the streams manager 350 shown inFIG. 3. The streams manager 350 is software that manages one or morestreaming applications, including creating operators and data flowconnections between operators in an operator graph that represents astreaming application. The streams manager 350 includes a performancemonitor 410 and a tuple breakpoint mechanism 450. The performancemonitor 410 preferably includes a tuple monitor 420, one or morethresholds 430, and one or more historical logs 440. The tuple monitor420 monitors data tuples processed by operators in an operator graph,and logs the monitored information in historical log(s) 440. Thethresholds 430 may include any suitable criteria, including criteriathat determines when a tuple breakpoint fires. Thus, in oneimplementation, the performance monitor 410 may compare the data in thehistorical log(s) 440 to the threshold(s) 430 and may then signal thetuple breakpoint mechanism 450 that the threshold(s) 430 have beensatisfied, causing a tuple breakpoint to fire in the tuple breakpointmechanism 450. In an alternative implementation, the threshold(s) 430 inthe performance monitor are not related to breakpoint processing, withthe tuple breakpoint mechanism 450 determining when a defined tuplebreakpoint 460 fires based on information in the historical log(s) 440of the performance monitor. The tuple breakpoint 460 preferablyspecifies at least one breakpoint criterion. Suitable examples ofbreakpoint criteria for tuple breakpoint 460 are shown in FIG. 5 toinclude CPU usage 510, memory usage 520, disk usage 530, energy usage540, and other resource usage 550. CPU usage 510 preferably specifies athreshold amount of CPU usage for a data tuple or a defined set of datatuples. This CPU usage could be specified from the time the data tuplewas created, or from the time the data tuple was first operated on by anoperator in the operator graph. The CPU usage can be specified in anysuitable way, including processor-seconds, cycles, time-slices,bogomips, etc.

The memory usage 520 for a data tuple specifies a threshold for memoryusage by a data tuple or a defined set of data tuples. The memory usage520 can be specified in any suitable way, including memory size such asmegabytes, memory size as a percentage of system resources, a percentageof virtual memory allocated to a virtual machine, number of memoryaccesses, number of access to certain type of memory, or amount ofmemory of a certain type of memory, i.e., flash etc.

The disk usage 530 for a data tuple specifies a threshold for disk usageby a data tuple or a defined set of data tuples. The disk usage 530 canbe specified in any suitable way, including number of disk reads, numberof disk writes, average size of read or write, number of disk reads orwrites to specific disks, disk size such as megabytes, a percentage of avirtual disk allocated to a virtual machine, etc.

The energy usage 540 for a data tuple specifies a threshold for energyusage by a data tuple or a defined set of data tuples. The energy usage540 can be specified in any suitable way, including milliwatts, joules,British Thermal Units (BTUs), a percentage of energy used by a virtualmachine, etc.

Other resource usage 550 represents that usage of other resources couldalso be criteria for defining a tuple breakpoint 460. Other resourceusage 550 could include, for example, usage of a physical or virtualLAN, or usage of any other suitable resource in a computer system,either hardware or software, whether currently known or developed in thefuture. The criteria in FIG. 5 for tuple breakpoint 460 are shown by wayof example, and are not limiting. The disclosure and claims hereinexpressly extend to any suitable criteria, including combinations ofcriteria, that can determine when a tuple breakpoint fires based onusage of one or more resources in a computer system by a data tuple or aset of data tuples.

Referring again to FIG. 4, the tuple breakpoint 460 may specify at leastone tuple set criterion, shown in FIG. 4 is tuple set criteria 462,which groups data tuples into a tuple set. Examples of suitable tupleset criteria 462 are shown in FIG. 6 to include a specified data tupleand all data tuples created from the specified data 610; a manual tuplegroup 620, which represents a data tuple group manually defined by asystem administrator; an automatic tuple group 630, which represents adata tuple group automatically defined according to some specifiedcriterion or criteria; and other tuple group 640, which represents anysuitable grouping of data tuples. The set criteria 462 allows definingtuple sets for which resource usage may be monitored and for whichbreakpoints may be defined. A simple example will illustrate. Let'sassume the tuple set criteria 462 includes a specified data tuple andall data tuples created from the specified data tuple 610. For thisspecific example, a breakpoint could be defined for this tuple set,which means the resource usage by all of the data tuples in the tupleset is monitored and added, and when the resource usage specified in thebreakpoint for this tuple set is exceeded, the breakpoint fires. In analternative implementation, a tuple set may be defined, and a breakpointcan fire when resource usage by any of the data tuples in the tuple setcauses the tuple breakpoint to fire. The concept of a tuple set thusallows both monitoring of resource usage by the performance monitor 410over multiple data tuples in a tuple set, and defining one or morebreakpoints in terms of resource usage of all of the data tuples in thetuple set.

Referring to FIG. 4, the tuple breakpoint mechanism 450 includes anoperator halt mechanism 470 that is used when a tuple breakpoint 460fires. The operator halt mechanism 470 defines at least one haltcriterion, shown as halt criteria 472 in FIG. 4, which specifies one ormore operators to halt when a breakpoint fires. Suitable examples ofhalt criteria 472 are shown in FIG. 7 to include halt all operators 710;halt a defined list of operators 720; halt an operator that caused abreakpoint 730 to fire; halt an operator that used the most of aspecified resource 740; and halt an operator that executed the datatuple the greatest number of times 750. Note that halting a defined listof operators 720 can include halting all operators 710 when alloperators are on the defined list. In one suitable implementation, eachdefined tuple breakpoint 460 has its own corresponding halt criteria472. However, in a different implementation, halt criteria 472 couldapply to all breakpoints, or to a specified subset of breakpoints.

The tuple breakpoint display mechanism 480 in FIG. 4 displays to a usertuple breakpoint display information 482. Examples of the tuplebreakpoint display information 482 are shown in FIG. 8 to include: tupleresource usage 810; time a data tuple spent in the operator graph 820;time the data tuple spent waiting in the operator graph 830; a list ofoperators that processed the data tuples and how many times 840; and theflow of the data tuple through the operator graph 850. This informationcan help a user debug a streaming application when data tuples are usingan excessive amount of resources, as defined by the breakpoint criteriain the tuple breakpoint.

FIG. 9 shows a method 900 for defining a tuple breakpoint, such as tuplebreakpoint 460 shown in FIGS. 4 and 5. At least one breakpoint criterionis defined (step 910). At least one halt criterion is defined (step920). Tuple breakpoint display information is also defined (step 930).Method 900 is then done. With the tuple breakpoint defined as shown inmethod 900 in FIG. 9, the streaming application can be executed. Whenthe breakpoint criteria defined in step 910 is satisfied, the tuplebreakpoint fires.

Referring to FIG. 10, a method 1000 shows what happens when a tuplebreakpoint fires. As long a no tuple breakpoint fires (step 1010=NO),method 1000 loops back and continues monitoring. When a tuple breakpointfires (step 1010=YES), one or more operators in the operator graph arehalted according to the defined halt criteria (step 1020), and the tuplebreakpoint display information is displayed (step 1030). Method 1000 isthen done.

A simple example is provided in FIGS. 11-13 to illustrate the conceptsdiscussed above. A simple operator graph 1100 is shown in FIG. 11 toinclude four operators, A, B, C and D. Operator A is a source of datatuples. Operator B processes data tuples received from Operator A andalso processes data tuples received from Operator C. Operator Cprocesses data tuples received from Operator B, and outputs data tupleseither to Operator D or to Operator B for further processing. In thissimple example, we assume Operator C includes logic to determine whethera data tuple has been sufficiently processed or not. If a data tuple hasbeen sufficiently processed, the data tuple is output to Operator D,which is a sink for operators. If the data tuple has not beensufficiently processed, the data tuple is fed back to Operator B, whichprocesses the data tuple again. Because of this feedback path fromoperator C to Operator B, it is possible for data tuples to usesignificant resources, especially after repeated loops through theoperator graph. Of course, in other configurations, it is possible for atuple to use significant resources even when the tuple does not feedback in the operator graph. The tuple breakpoint 460 defined hereinallows a breakpoint to fire when resource usage by a tuple or a tupleset exceeds some defined threshold or criteria.

Referring to FIG. 12, we assume for this simple example a tuplebreakpoint 1210 is defined that fires when a tuple or defined tuple setuses more than 100 MB of memory 1220. Tuple breakpoint 1210 in FIG. 12is one suitable example for tuple breakpoint 460 shown in FIGS. 4 and 5.This means a threshold of 100 MB of memory is set for a tuple or tupleset. As long as a tuple or tuple set uses less than 100 MB of memory,the tuple breakpoint 1210 does not fire. When the usage of memory by atuple or tuple set exceeds the 100 MB threshold defined at 1220, thebreakpoint fires to indicate the tuple or tuple set has used more memorythan the specified memory threshold. For the simple example in FIGS.11-13, we assume halt criteria 1310 in FIG. 13 is defined that halts alloperators 710. Thus, when the tuple breakpoint 1210 fires when the tupleor tuple set uses more than 100 MB of memory, all operators, namely, A,B, C and D in the operator graph 1100, are halted. Once all theoperators are halted, any suitable information can be displayed to auser, including any or all of the tuple breakpoint display info shown inFIG. 8. Of course, other information not shown in FIG. 8 could also bedisplayed to the user when a breakpoint fires.

The tuple breakpoint mechanism disclosed and claimed herein provides anincredibly powerful and flexible way to debug streaming applications bydetecting when data tuples use an amount of resources that exceeds athreshold set in one or more defined tuple breakpoints.

The disclosure and claims herein relate to a streams manager thatmonitors data tuples processed by a streaming application represented byan operator graph. The streams manager includes a tuple breakpointmechanism that allows defining a tuple breakpoint that fires based onresource usage by the data tuple. When the tuple breakpoint fires, oneor more operators in the operator graph are halted according tospecified halt criteria. Information corresponding to the breakpointthat fired is then displayed. The tuple breakpoint mechanism thusprovides a way to debug a streaming application based on resource usageby data tuples.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

The invention claimed is:
 1. A computer-implemented method executed by at least one processor for debugging a streaming application, the method comprising: executing a streaming application that comprises an operator graph that includes a plurality of operators that process a plurality of data tuples; monitoring resource usage of at least one computer system resource by each of the plurality of data tuples processed by the plurality of operators in the operator graph, wherein the resource usage for a selected data tuple comprises a usage of the at least one computer system resource during the processing of the selected data tuple by at least two of the plurality of operators that process the selected data tuple; defining a tuple breakpoint that defines at least one criterion for usage of the at least one computer system resource by at least one data tuple, and fires when a data tuple satisfies the at least one criterion for usage of the at least one computer system resource; and when the tuple breakpoint fires, halting at least one operator in the operator graph and displaying information regarding a data tuple that caused the tuple breakpoint to fire.
 2. The method of claim 1 wherein the resource usage comprises CPU usage and the at least one criterion for usage of the at least one resource specifies CPU usage.
 3. The method of claim 1 wherein the resource usage comprises memory usage and the at least one criterion for usage of the at least one resource specifies memory usage.
 4. The method of claim 1 wherein the resource usage comprises disk usage and the at least one criterion for usage of the at least one resource specifies disk usage.
 5. The method of claim 1 wherein the resource usage comprises energy usage and the at least one criterion for usage of the at least one resource specifies energy usage.
 6. The method of claim 1 wherein the at least one criterion for usage of the at least one resource specifies usage of the at least one resource for a defined set of multiple data tuples in the operator graph.
 7. The method of claim 1 further comprising halting an operator in the operator graph that caused the tuple breakpoint to fire.
 8. The method of claim 1 further comprising displaying information regarding a data tuple that caused the tuple breakpoint to fire, wherein the displayed information comprises tuple resource usage of at least one computer system resource.
 9. The method of claim 8 wherein the display information further comprises at least one of: time the data tuple that caused the tuple breakpoint to fire spent in the operator graph; time the data tuple that caused the tuple breakpoint to fire was waiting in the operator graph; list of operators that processed the data tuple that caused the tuple breakpoint to fire and how many times; and flow of the data tuple that caused the tuple breakpoint to fire through the operator graph. 